Materiais

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RotuLabic

RotuLabic is a system developed to support manual labeling of documents. The system uses a transductive learning algorithm to recommend labels to the user and, thus, supports the manual labeling work. Currently, the system interface is available only in Portuguese.

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ICT - Inductive Classification Tool

Inductive Classification Tool was developed in Java language and aims to generate results using traditional inductive algorithms and their different parameter for datasets represented in ARFF format.

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TPT - Text Preprocessing Tool

This is a Java tool which transforms text files in a document-term matrix.

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SKET - Statistical Keyword Extraction Tool

This tool extracts keywords from single documents using statistical methods.

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FEATuRE - Features gEnerator based on AssociaTion RulEs

The required steps to generate the bag-of-related words are implemented in this tool. Thre is also functionalities to analyse the generated bag-of-related-words.

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Mldatagen - A multi-label dataset generator

This framework, which is described in ICMC-USP technical report, can to generate synthetic multi-label datasets using two strategies: hyperspheres or hypercubes. For each label in a dataset, these strategies randomly generate a geometric shape (hypersphere or hypercube), which is populated with points (instances or examples) randomly generated. Afterwards, each instance is labeled according to the shapes it belongs to, which defines the instance multi-label.

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CoAL and Co-Training Java Implementation

CoAL is a new algorithm which merges Co-Training, a well known multi-view semi-supervised machine learning algorithm, with Co-Testing, a multi-view active learning algorithm. CoAL, as well as Co-Training, were implemented in Java using the Weka API. In addition to these algorithms, the implementation offers an abstract class that eases the task of implementing new Co-Training style algorithms.

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Gráficos Labic

Gráficos Labic is a very simple tool which helps the user to quickly choose from a set of graphs commonly used in the pre-processing phase of the Data Mining process, as well as retrieving the correspondent R code of the chosen graph.

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Torch - Topic Hierarchies

Torch helps users to “see hidden topics” in text collections. This tool can be used in a wider variety of applications such as digital libraries, web directories and document engineering. Torch is based on the IHTC - Incremental Hierarchical Term Cluster method, which aims to build topic hierarchies from growing text collections.

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